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IISE Transactions is a peer-reviewed journal published by Taylor & Francis, covering Supply Chain and Inventory Management, Reliability and Maintenance Optimization, Scheduling and Optimization Algorithms.
| Publisher | Taylor & Francis |
|---|---|
| Reference style | Author–year (Chicago, T&F) Author–year — (Smith, 2023) in the text Smith, Ada, Ben Jones, and Cara Lee. 2023. "A Representative Article Title." IISE Transactions 12 (3): 45–58.
Formats any DOI in IISE Transactions style. No sign-up. |
| Publishes research in | Supply Chain and Inventory Management Reliability and Maintenance Optimization Scheduling and Optimization Algorithms Advanced Statistical Process Monitoring Advanced Manufacturing and Logistics Optimization |
| ISSN | 2472-5854 |
| Citation impact (2-yr) | 2.54 |
| h-index | 49 |
| i10-index | 378 |
| Total citations | 13,422 |
| Top institutions publishing here | Georgia Institute of Technology |
| Journal website | www.tandfonline.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
The modern manufacturing industry is investing in new technologies such as the Internet of Things (IoT), big data analytics, cloud computing and cybersecurity to cope with system complexity, increase information visibility, improve production performance, and gain competitive advantages in the global market. These advances are rapidly enabling a new generation of smart manufacturing, i.e., a…
The aim of this article is to analyze and review the scientific literature relating to the application of Augmented Reality (AR) technology in industry. AR technology is becoming increasingly diffuse, due to the ease of application development and the widespread use of hardware devices (mainly smartphones and tablets) able to support its adoption. Today, a…
One major challenge of implementing Directed Energy Deposition (DED) Additive Manufacturing (AM) for production is the lack of understanding of its underlying process–structure–property relationship. Parts manufactured using the DED technologies may be too inconsistent and unreliable to meet the stringent requirements for many industrial applications. The objective of this research is to characterize the underlying…
The goal of this work is to achieve the defect-free production of parts made using Additive Manufacturing (AM) processes. As a step towards this goal, the objective is to detect flaws in AM parts during the process by combining predictions from a physical model (simulation) with in-situ sensor signatures in a machine learning framework. We…
Deep learning has emerged as a powerful tool to model complicated relationships between inputs and outputs in various fields including degradation modeling and prognostics. Existing deep learning-based prognostic approaches are often used in a black-box manner and provide only point estimations of remaining useful life. However, accurate interval estimations of the remaining useful life are…
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